Black and Latino gay, bisexual, and men who have sex with men (GBM) continue to face higher rates of HIV compared to White GBM. Intersectional discrimination is an important barrier to oral pre-exposure prophylaxis (PrEP) uptake, but its association with long-acting injectable (LAI) PrEP preferences remains understudied. We conducted a cross-sectional study of 433 cisgender GBM in Philadelphia between June 2021 to March 2022. Using logistic regression with marginal standardization, we examined associations between intersectional discrimination and LAI PrEP preferences, testing effect modification by ethnoracial group. Preference for LAI PrEP over daily oral (76

Health and health disparities vary substantially by geography, including geopolitical boundaries such as United States congressional districts. Every ten years congressional districts for the House of Representatives are redistricted, but occasionally the Courts step in and force states to redistrict gerrymandered congressional maps. Analyses of court mandated redistricting decisions often focus on the distribution of voters by political party and race, but less is known about how health and health disparities are distributed across congressional districts before and after redistricting. In this analysis, we examine how the magnitude of disparities varied between and within congressional districts in Pennsylvania, before and after the state Supreme Court of Pennsylvania’s decision ordering a redistricting in 2018 that produced less politically gerrymandered districts. Using georeferenced vital statistics data from 2013–2015 (before the redistricting), we explore levels of and disparities in infant mortality rates (IMR) and deaths of despair (DoD) using boundaries from before (Congresses 113–115) and after (Congress 116) this redistricting. Using consistent mortality data (2013–2015) and boundaries from before and after the 2018 redistricting, we find that after redistricting disparities in infant mortality and deaths of despair between congressional districts were slightly wider for all educational groups except for those with less than a high school degree, and slightly narrower for all racial-ethnic groups other than for Hispanic and non-Hispanic White populations, compared with before redistricting. Understanding how disparities vary between and within districts after redistricting can inform our understanding of the relationships between geopolitical boundaries, election processes, and health disparities.

Background:Between 2010 and 2024, 120 000-710 000 people were hospitalized for influenza in the United States each year. In the current century there has been one influenza pandemic so far, causing widespread infection, hospitalization, and deaths. While prior research has examined neighborhood and individual racial and socioeconomic disparities in influenza outcomes during seasonal or pandemic influenza, there is limited understanding of whether the associations between neighborhood social determinants and influenza outcomes differ between seasonal and pandemic influenza. Methods:Using Health Care Utilization Project hospitalization data from New York State from 2009-2013, covering the 2009 H1N1 pandemic and seasonal influenza in the following years, we computed the relative index of inequality (RII) in influenza hospitalization rates associated with neighborhood social and economic factors. Results:All neighborhood factors showed significant associations with hospitalization, although the magnitude of the disparities differed by neighborhood factor, and disparities were generally slightly wider in pandemic influenza periods, although differences between periods were small. The widest hospitalization rate disparity for pandemic and seasonal influenza periods was for median household income (pandemic RII, 2.39 [95% confidence interval, 2.16-2.63]; seasonal RII, 2.34 [2.10-2.60]); the factor with the largest difference between pandemic and seasonal periods was poverty (pandemic and seasonal RIIs, 2.11 and 1.84, respectively). Conclusions:Our findings demonstrate the persistence of neighborhood inequities in influenza hospitalizations in both pandemic and seasonal periods and suggest the importance of investing in structural conditions to address health inequities.

ABSTRACT The goal of this article is to summarize common methods of antibiotic measurement used in clinical research and demonstrate analytic methods for selection of exposure variables. Variable selection was demonstrated using three methods for modeling exposure, using data from a case-control study on Clostridioides difficile infection in hospitalized patients: 1) factor analysis of mixed data, 2) multiple logistic regression models, and 3) Least Absolute Shrinkage and Selection Operator (LASSO) regression. The factor analysis identified 9 variables contributing the most variation in the dataset: any antibiotic treatment ; number of classes ; number of treatments ; dose ; and classes monobactam , β -lactam β -lactamase inhibitors , rifamycin , carbapenem , and cephalosporin . The regression models resulting in the best model fit used predictors any antibiotic exposure and proportion of hospitalization on antibiotics . The LASSO model selected 22 variables for inclusion in the predictive model, exposure variables including: any antibiotic treatment ; classes β -lactam β -lactamase inhibitors , carbapenem , cephalosporin , fluoroquinolone , monobactam , rifamycin , sulfonamides , and miscellaneous ; and proportion of hospitalization on antibiotics . Investigators studying antibiotic exposure should consider multiple aspects of treatment informed by their research question and the theory on how antibiotics may impact the distribution of the outcome in their target population.

Many patients experience unexpected harm while receiving healthcare, with a lasting impact on patients, families, and caregivers. Communication and Resolution Programs are being adopted with increased frequency, as a more systematic, transparent, and equitable approach to these unexpected outcomes. The aim of this study was to identify whether demographic factors played a role in identifying patients with unexpected death, as managed in our CRP. This nested case-controlled compared 236 patients who experienced an unanticipated death with 2,360 controls who died expectedly over a 10-year period. Patients with unexpected death were more likely to be Black (AOR 2.18 95% CI 1.01–4.68), higher comorbidity burden (AOR 1.07 per additional co-morbidity, 95% OR 1.01–1.14), and a lower Relative Expected Mortality (AOR: 5.39; 95% CI: 1.76–16.55). Awareness of these demographic risk factors for unexpected mortality may lead to changes in how these patients are evaluated and treated. Communication and Resolution Programs can be used to identify the patients at the highest risk for unexpected outcomes.

OBJECTIVE:To deconstruct the multiple levels of risk factors for Clostridioides difficile infection, using multilevel models (MLMs) accounting for patient movement. STUDY DESIGN AND SETTING:Case-control study of patients hospitalized in three acute care Delaware hospitals, December 2019-December 2023. PATIENTS:Cases were patients aged ≥18 years who tested positive for hospital-onset C. difficile infection. Controls were patients aged ≥18 years hospitalized more than 72 hours, who did not test positive for C. difficile infection. METHODS:Hierarchical and cross-classified MLMs were used to calculate odds of C. difficile infection based on patient-level risk factors and to evaluate the variation in odds of infection attributable to environmental risk factors using the hospital unit(s) a patient was assigned to during hospitalization. RESULTS:Our study included 1,223 patients (249 cases, 974 controls). In both models, greater odds of infection were associated with antibiotic exposure [adjusted odds ratio (aOR) = 11.20, 95% confidence interval (CI) = 7.19, 17.40; aOR = 12.80, 95% CI = 8.46, 19.40 for hierarchical and cross-classified models respectively] and health insurance (aOR = 1.74, 95% CI = 1.12, 2.68; aOR = 1.62, 95% CI = 1.03, 2.53; public vs. private). Median odds ratios (MOR) for both models indicated greater relevance of between-unit heterogeneity in the outcome than health insurance but less than antibiotic exposure (MOR = 1.83, 95% CI = 1.56, 2.30 and 2.71 95% CI = 2.10, 4.06). CONCLUSION:Using multilevel methods accounting for patient movement, we found that while antibiotic use is the most important risk factor in patients that developed C. difficile infection, environmental risk factors are additionally important and should be considered in research involving hospitalized patients and healthcare-associated infections.

The increasing availability and accessibility of electronic health record (EHR) data has made it a rich secondary source to conduct comparative effectiveness studies. To perform such studies, many researchers are turning to the target trial framework (TTF) to emulate the hypothetical randomized clinical trial. The quality of this emulation depends, in part, on the availability and accessibility of data for each component of the TTF. Yet one overarching challenge with using EHR data is that unstructured fields, such as clinical encounter notes, contain copious details on the patient yet require additional steps to extract if needed in the conduct of the study. Natural language processing (NLP) represents a spectrum of methods to assist with automating this extraction, from simpler rule-based methods to machine learning and artificial intelligence approaches that can handle complex language structures. What follows is a discussion on how NLP methods can augment information and data for researchers looking to estimate a treatment effect using EHR data via the TTF to emulate the hypothetical clinical trial. We conclude with recommendations for researchers interested in using NLP methods to obtain data stored in the free text of the EHR as well as considerations regarding the quality and validity of this data for the TTF.

Electronic health record (EHR) data have become essential and commonplace in epidemiological and clinical research. In this narrative review on the use of EHR data in epidemiology, I discuss appropriate research questions, common biases, and potential sensitivity analyses focusing on recent work that has been done to improve the internal and external validity of EHR-based studies. An appropriate research question addresses issues of EHR-data availability and accessibility, while patient selection forces into healthcare may result in a sample that lacks representativeness. Natural language processing tools are becoming widespread and tailored to EHR use for operationalizing unstructured data. Common biases identified in the literature include misclassification and measurement error, informed presence bias, selection bias and sampling error, and residual confounding. EHR data are unlike other observational data sources and carry assumptions about patient selection and clinical documentation that can impact the validity of the analyses. Potential sensitivity analyses including quantitative bias analysis can help to understand the impact of one or more of these biases on the study findings.

There has been a proliferation of large-scale electronic health record (EHR) data platforms that pool across multiple healthcare organizations, such as the National Institutes of Health's All of Us in the federal space and TriNetX and Epic Cosmos in the commercial space. There are unique issues that occur when EHR data are aggregated across disparate healthcare systems beyond the general-and more well known-concerns about secondary analysis of EHR data from a single entity. In this article, we define aggregated EHR data, contrasting it to other real-world data sources, highlight benefits and challenges when working with aggregated EHR data, offer several "good practices" to address these challenges, and conclude by discussing whether it is appropriate to pool these data together or not.

Purpose To validate the National Provider Identifier (NPI), a commonly used data source in health services research, for identifying primary care physicians, physician assistants (PAs), and nurse practitioners (NPs). Methods Validation studies to calculate the sensitivity, specificity, and associated 95 % confidence intervals for physicians, PAs, and NPs. For physicians, Medicare claims data were used as an imperfectly measured referent standard. For PAs and NPs, we used a simulation-based method to estimate accuracy parameters that assumed the NPI and Medicare claims were equally misclassified. Results Using the Medicare claims as the referent standard for physicians yielded a sensitivity and specificity of 0.95 (95 % CI: 0.88, 0.98) and 0.76 (95 % CI: 0.73, 0.79), respectively. Using the simulation-based method yielded a sensitivity and specificity of 0.57 (95 % CrI: 0.11, 0.97) and 0.56 (95 % CrI: 0.10, 0.96), respectively for PAs and 0.58 (95 % CrI: 0.13, 0.97) and 0.61 (95 % CrI: 0.14, 0.97), respectively for NPs. Conclusions Our validation results varied by provider role. Accuracy was highest for physicians further highlighting the challenges in quantifying PAs and NPs based on their NPI alone. Failure to consider potential misclassification in the NPI may result in biased research findings.

Electronic health records (EHRs) have become ubiquitous in clinical practice. Given the rich biomedical data captured for a large panel of patients, secondary analysis of these data for health research is also commonplace. Yet, there are many caveats to EHR data that the researchers must be aware of, such as the accuracy of and motive for documentation, and the reason for patients’ visits to the clinic. The clinician—the author of the documentation—is thus central to the correct interpretation of EHR data for research purposes. In this study, I interviewed 11 physicians in various clinical specialties to bring attention to their view on the validity of research using EHR data. Qualitative, in-depth, one-on-one interviews were conducted with practicing physicians in inpatient and outpatient medicine. Content analysis using a data-driven, inductive approach to identify themes related to challenges and opportunities in the reuse of EHR data for secondary analysis generated seven themes. Themes that reflected challenges of EHRs for research included (1) audience, (2) accuracy of data, (3) availability of data, (4) documentation practices, and (5) representativeness. Themes that reflected opportunities of EHRs for research included (6) endorsement and (7) enablers. The greatest perceived barriers reflected the intended audience of the EHR, the interpretation and meaning of the data, and the quality of the data for research purposes. Physicians generally expressed more perceived challenges than opportunities in the reuse of EHR data for research purposes; however, they remained optimistic.

Introduction: Predictive models for Clostridioides difficile infection can identify high -risk patients and aid clinicians in preventing infection. Issues of generalizability regarding current predictive models have been acknowledged but, to the authors' knowledge, have never been quantified. Methods: C. difficile infection, severity and recurrence predictive models were created using multi-variate logistic regression through case -control sampling from an urban safety -net hospital. Models were validated using five -fold cross -validation, and inverse probability weights (IPW) based on two different catchment area definitions were used to improve external validity. Akaike Information Criterion (AIC), area under the receiver operating characteristic curve (AUROC), and sensitivity and specificity with bootstrapped confidence intervals (CI) were used to assess and compare model fit and performance. Results: Changes in performance before and after weighting were small across all models, although differences were more apparent after weighting the recurrence model (AUROC values of 0.78, 0.76 and 0.71 for the unweighted and two weighted models, respectively). Overall, the infection model performed the best (AUROC 0.82, 95% CI 0.78-0.85), followed by the recurrence model (AUROC 0.78, 95% CI 0.69-0.86) and then the severity model (AUROC 0.70, 95% CI 0.63-0.78). Conclusions: The performance of the models after weighting did not change drastically, suggesting that the models predicting C. difficile infection, severity and recurrence may not be impacted by patient selection factors. However, other researchers may wish to consider addressing these catchment forces using IPW. (c) 2024 The Author(s). Published by Elsevier Ltd on behalf of The Healthcare Infection Society. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

PURPOSE HIV pre-exposure prophylaxis (PrEP) may increase rates of bacterial sexually transmitted infections (STIs) among gay, bisexual, and other men who have sex with men (GBM) through risk compensation (eg, an increase in condomless sex or number of partners); however, longitudinal studies exploring the time-dependent nature of PrEP uptake and bacterial STIs are limited. We used marginal structural models to estimate the effect of PrEP uptake on STI incidence. METHODS We analyzed data from the iCruise study, an online longitudinal study of 535 Ontarian GBM from July 2017 to April 2018, to estimate the effects of PrEP uptake on incidence of self-reported bacterial STIs (chlamydia, gonorrhea, and syphilis) collected with 12 weekly diaries. The incidence rate was calculated as the number of infections per 100 person-months, with evaluation of the STIs overall and individually. We used marginal structural models to account for time-varying confounding and quantitative bias analysis to evaluate the sensitivity of estimates to nondifferential outcome misclassification. RESULTS Participating GBM were followed up for a total of 1,623.5 person-months. Overall, 70 participants (13.1%) took PrEP during the study period. Relative to no uptake, PrEP uptake was associated with an increased incidence rate of gonorrhea (incidence rate ratio = 4.00; 95% CI, 1.67-9.58), but not of chlamydia or syphilis, and not of any bacterial STI overall. Accounting for misclassification, the median incidence rate ratio for gonorrhea was 2.36 (95% simulation interval, 1.08-5.06). CONCLUSIONS We observed an increased incidence rate of gonorrhea associated with PrEP uptake among Ontarian GBM that was robust to misclassification. Although our findings support current guidelines for integrating gonorrhea screening with PrEP services, additional research should consider the long-term impact of PrEP among this population.

Table 0; documenting the steps to go from clinical database to research datasetJournal of Clinical EpidemiologyVol. 170PreviewData-driven decision support tools have been increasingly recognized to transform health care. However, such tools are often developed on predefined research datasets without adequate knowledge of the origin of this data and how it was selected. How a dataset is extracted from a clinical database can profoundly impact the validity, interpretability and interoperability of the dataset, and downstream analyses, yet is rarely reported. Therefore, we present a case study illustrating how a definitive patient list was extracted from a clinical source database and how this can be reported. Full-Text PDF Open Access

BackgroundPopulation viral load (VL), the most comprehensive measure of the HIV transmission potential, cannot be directly measured due to lack of complete sampling of all people with HIV. ObjectiveA given HIV clinic’s electronic health record (EHR), a biased sample of this population, may be used to attempt to impute this measure. MethodsWe simulated a population of 10,000 individuals with VL calibrated to surveillance data with a geometric mean of 4449 copies/mL. We sampled 3 hypothetical EHRs from (A) the source population, (B) those diagnosed, and (C) those retained in care. Our analysis imputed population VL from each EHR using sampling weights followed by Bayesian adjustment. These methods were then tested using EHR data from an HIV clinic in Delaware. ResultsFollowing weighting, the estimates moved in the direction of the population value with correspondingly wider 95% intervals as follows: clinic A: 4364 (95% interval 1963-11,132) copies/mL; clinic B: 4420 (95% interval 1913-10,199) copies/mL; and clinic C: 242 (95% interval 113-563) copies/mL. Bayesian-adjusted weighting further improved the estimate. ConclusionsThese findings suggest that methodological adjustments are ineffective for estimating population VL from a single clinic’s EHR without the resource-intensive elucidation of an informative prior.

Introduction: Early in the COVID-19 pandemic, routine sexually transmitted infection (STI) screenings decreased, and test positivity rates increased due to limited screening appointments, national-level STI testing supply shortages, and social distancing mandates. It is unclear if adolescent preventive STI screening has returned to pre-pandemic levels and if pre-existing disparities worsened in late-pandemic.Methods: This cross-sectional study examined 22,974 primary care visits by 13-19-year-olds in the Philadelphia metropolitan area undergoing screening for gonorrhea and chlamydia in a 31-clinic pediatric primary care network during 2018-2022. Using interrupted-time-series analysis and logistic regression, pandemic-related changes in the asymptomatic STI screening rate and test positivity were tracked across patient demographics. Neighborhood moderation was investigated by census-tract-level Child Opportunity Index in 2023.Results: The asymptomatic STI screening rate dropped by 27.8 percentage points (pp) and 13.5pp when the pandemic and national STI test supply shortage began, respectively, but returned to pre-pandemic levels after supply availability was restored in early 2021. Non-Hispanic-Black adolescents had a significant pandemic drop in STI screening rate, and it did not return to pre-pandemic levels (-3.6 pp in the late-pandemic period, p< 0.01). This decrease was more pronounced in socioeconomically and educationally disadvantaged neighborhoods (7.5 pp and 9.9 pp lower, respectively) than in advantaged neighborhoods (both p< .001), controlling for sex, age, insurance type and clinic characteristics.Conclusions: Neighborhood socioeconomic and educational disadvantage amplified racial-ethnic disparities in STI screening during the pandemic. Future interventions should focus on improving primary care utilization of non-Hispanic-Black adolescents to increase routine STI screening and preventive care utilization.

Many ecological studies examine health outcomes and disparities using administrative boundaries such as census tracts, counties, or states. These boundaries help us to understand the patterning of health by place, along with impacts of policies implemented at these levels. However, additional geopolitical units (units with both geographic and political meaning), such as congressional districts (CDs), present further opportunities to connect research with public policy. Here we provide a step-by-step guide on how to conduct disparities-focused analysis at the CD level. As an applied case study, we use geocoded vital statistics data from 2010-2015 to examine levels of and disparities in infant mortality and deaths of despair in the 19 US CDs of Pennsylvania for the 111th-112th (2009-2012) Congresses and 18 CDs for the 113th-114th (2013-2016) Congresses. We also provide recommendations for extending CD-level analysis to other outcomes, states, and geopolitical boundaries, such as state legislative districts. Increased surveillance of health outcomes at the CD level can help prompt policy action and advocacy and, hopefully, reduce rates of and disparities in adverse health outcomes.

Background Despite a 40% reduction in breast cancer mortality over the last 30 years, not all groups have benefited equally from these gains. A consistent link between later stage of diagnosis and disparities in breast cancer mortality has been observed by race, socioeconomic status, and rurality. Therefore, ensuring equitable geographic access to screening mammography represents an important priority for reducing breast cancer disparities. Access to breast cancer screening was evaluated in Delaware, a state that experiences an elevated burden from breast cancer but is otherwise representative of the US in terms of race and urban–rural characteristics. We first conducted a catchment analysis of mammography facilities. Finding evidence of disparities by race and rurality, we next conducted a location-allocation analysis to identify candidate locations for the establishment of new mammography facilities to optimize equitable access. Methods A catchment analysis using the ArcGIS Pro Service Area analytic tool characterized the geographic distribution of mammography sites and Breast Imaging Centers of Excellence (BICOEs). Poisson regression analyses identified census tract-level correlates of access. Next, the ArcGIS Pro Location-Allocation analytic tool identified candidate locations for the placement of additional mammography sites in Delaware according to several sets of breast cancer screening guidelines. Results The catchment analysis showed that for each standard deviation increase in the number of Black women in a census tract, there were 68% (95% CI 38–85%) fewer mammography units and 89% (95% CI 60–98%) fewer BICOEs. The more rural counties in the state accounted for 41% of the population but only 22% of the BICOEs. The results of the location-allocation analysis depended on which set of screening guidelines were adopted, which included increasing mammography sites in communities with a greater proportion of younger Black women and in rural areas. Conclusions The results of this study illustrate how catchment and location-allocation analytic tools can be leveraged to guide the equitable selection of new mammography facility locations as part of a larger strategy to close breast cancer disparities.